Observability usually starts as an infrastructure word. Logs, metrics, traces, dashboards, alerts. For machine learning, that definition is too narrow.

ML systems need infrastructure observability, but they also need run context: which code ran, which data version was used, which parameters changed, which artifact was produced, which GPU was idle, and which model version is now serving traffic.

Training pipeline observability

Training pipelines interact with data warehouses, object stores, feature pipelines, GPUs, distributed workers, and model registries. A failure can come from application code, data quality, credentials, resource limits, networking, image builds, or a flaky dependency.

Good observability gives teams the shortest path from symptom to cause.

Logs

Logs are still the first debugging tool. Python libraries, data loaders, training frameworks, and application code all emit information that can explain a failure. In Kubernetes, those logs live with short-lived pods, so they need to be captured and attached to the run before the pod disappears.

For ML teams, logs are most useful when they are tied to run metadata. A raw pod name is less helpful than a run, project, component, queue, commit, and artifact lineage.

Failures and exceptions

Long-running pipelines fail. Memory limits, missing files, bad parameters, package conflicts, unavailable services, and data assumptions all show up eventually. Teams need direct access to failure state, exit codes, traceback output, and the workload definition that produced the failure.

Resource usage

ML workloads can waste expensive compute quietly. GPUs sit idle during data bottlenecks. CPU jobs request too much memory. Distributed workers wait for slow storage. Without resource visibility, teams optimize model code while the real problem sits in I/O or scheduling.

Useful signals include CPU, memory, GPU utilization, network throughput, ephemeral storage, queue wait time, and runtime duration.

Model and data behavior

Infrastructure health does not prove model health. Teams also need metrics, evaluation artifacts, prediction samples, drift checks, data summaries, and lineage. A model can run perfectly and still be wrong.

How Polyaxon helps

Polyaxon connects workload execution with ML metadata. Runs carry logs, statuses, parameters, artifacts, lineage, and runtime context in one place. That makes debugging less dependent on stitching together Kubernetes commands, object storage paths, and screenshots from separate systems.